WINGS: a WIde-field Nearby Galaxy-cluster Survey

c ESO 2009

Astronomy & Astrophysics manuscript no. wingsnir August 11, 2009

WINGS: a WIde-field Nearby Galaxy-cluster Survey.

arXiv:0902.0954v3 [astro-ph.CO] 11 Aug 2009

III. Deep near-infrared photometry of 28 nearby clusters ⋆ T. Valentinuzzi1 , D. Woods2,3 , G. Fasano4 , M. Riello5 , M. D’Onofrio1 , J. Varela4 , D. Bettoni4 , A. Cava4,6 , W.J. Couch7 , A. Dressler8 , J. Fritz4 , M. Moles9 , A. Omizzolo10,4 , B.M. Poggianti4 , and P. Kjærgaard11 1 2 3 4 5 6 7 8 9 10 11

Astronomy Department, University of Padova, Vicolo Osservatorio 3, 35122 Padova, Italy School of Physics, University of New South Wales, Sydney 2052, NSW, Australia Dept. of Physics & Astronomy, University of British Columbia, 6224 Agricultural Road, Vancouver, V6T 1Z1, BC, Canada INAF – Padova Astronomical Observatory, Vicolo Osservatorio 5, 35122 Padova, Italy Institute of Astronomy, Madingley Rd., Cambridge CB3 0HA Instituto de Astrofisica de Canarias, Via Lactea s.n., 38205 La Laguna, Tenerife, Spain Centre for Astrophysics & Supercomputing, Swinburne University, Hawthorn 3122, VIC, Australia Observatories of the Carnegie Institution of Washington, Pasadena, CA 91101, USA Instituto de Astrof´ısica de Andaluc´ıa (C.S.I.C.) Apartado 3004, 18080 Granada, Spain Specola Vaticana, 00120 Stato Citta’ del Vaticano The Niels Bohr Institute, Juliane Maries Vej 30, 2100 Copenhagen, Denmark

August 11, 2009 ABSTRACT

Context. This is the third paper in a series devoted to the WIde-field Nearby Galaxy-cluster Survey (WINGS). WINGS is a long-term project aimed at gathering wide-field, multiband imaging and spectroscopy of galaxies in a complete sample of 77 X-ray selected, nearby clusters (0.04 0.95), to choose the detection thresholds, and to quickly check the quality of the mosaics (astrometry, photometry, number counts ...), as explained in section 3.1; – extraction of several background stamps from the mosaic, useful to prepare a background image for photometric errors estimates with simulations. A collection of synthetic stars and galaxies (30% of exponential disks) is separately added to the background image, in an attempt to best reproduce the FWHM distribution of the real image. Detection and classification rates of stars and galaxies are computed separately (see Tab. 3) and are also used to fine tune the SExtractor input parameters (see section 3.1); – final running of SExtractor with the adjusted input parameters and partitioning of the main output catalog into the catalogs of stars, galaxies and unknown objects. The latter step is achieved here just relying upon SExtractor’s CLASS STAR, which ranges from 0 (galaxies) to 1 (stars), in the following way7 : 7 In paper-II the galaxies value was 0.2: the difference is due to the intrinsic differences between near-infrared and optical images. These values are estimated using simulations which reproduce the peculiar characteristics of the mosaics.

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Stars CLASS STAR ≥ 0.8 Galaxies CLASS STAR ≤ 0.35 Unknown 0.35 < CLASS STAR < 0.8; – since the CLASS STAR parameter is not reliable enough in all circumstances, in particular when the mosaic is affected by strong seeing variations, a further step is necessary in order to improve the assignment of each object to the right catalog and to remove from all of them the spurious detections. For this task we used a custom interactive tool which produces various diagnostic plots, and allows us to distinguish between stars and galaxies; – final “visual” (interactive) cleaning of the mosaic to correct any remaining blatant error both in detection and classification. In this phase the mosaic is displayed with the corresponding markers of stars and galaxies and, by visual inspection, the corrections are made directly on the image, saved on disk and then applied to the catalogs. In the following sections we will describe in more detail some of the aforementioned steps taken as part of the catalogs production. 3.1. Source Detection and Star/Galaxy Classification

The source detection was performed by running SExtractor on the final mosaics, convolved by 2D gaussian filters of size chosen to be equal to the median FWHM. In most cases, the low level of the background rms obtained with the drizzling techniques of the CASU pipeline, allowed us to use a 1.5σ clipping and a minimum area of 20 adjacent pixels as threshold parameters. These parameter values allowed us to simultaneously obtain a small number of spurious detections of stars and galaxies, and deep enough magnitude limits. In general, for each image, the right combination of threshold, minimum area and filter size was chosen relying upon the expected number counts of galaxies/stars and on serendipitous visual inspection of marked detections with the SAO-DS9 display tool. Spurious detections are typically misclassified as galaxies by SExtractor, and usually result from local background fluctuations and spikes or crosstalk of saturated stars. The preliminary star/galaxy classification was done relying upon SExtractor’s stellarity index (CLASS STAR). The median FWHM of the image to be processed (SEEING FWHM) is the chief parameter affecting the SExtractor’s star/galaxy separation algorithm. The sigma-clipping and filtering detection parameters used in SExtractor are also quite important, since background fluctuations and PSF distortions near the detection threshold can introduce uncertainties correlated with such quantities. Aiming to test on our mosaics both the detection capability of SExtractor and the reliability of its star/galaxy classifier, we have produced for each cluster a mock image of simulated stars and galaxies (a mix of 70% spheroidal r1/4 and of 30% exponential disks) with a sample background coming from the real image, and trying to match as far as possible the distribution of the stellar FWHM. With ARTDATA package, synthetic stars were modeled using Moffat profiles with β = 2.5. For spheroidal De Vaucouleurs and disk profiles, intrinsic half-light radii in the range 1-5 kpc and an Euclidean power law (n=0.3) luminosity function were adopted. Foucaud et al. (2007) claim that a 20% increase of the value of SEEING FWHM parameter had to be used to correctly match the synthetic stars added to the images, and this indicates the difficulty SExtractor has to correctly classify stars, at fainter magnitudes, even in good seeing conditions. We further investigated

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T. Valentinuzzi et al.: WINGS III: Near-Infrared Catalogs

Fig. 5. Distribution of the quantity τ = FWHMMIN ·∆FWHM of Eq.(1) for all the near-infrared mosaics. FWHMmax/min are the maximum and the minimum values of the median FWHM, calculated on the single detectors of the final stacked tiles, as shown in Fig.4. For all clusters with τ < 0.25 (the region delimited by the vertical dotted line) both the star/galaxy classification and the cleaning of the catalogs are quite straightforward.

this effect and found that, while the FWHMs of stars produced by the ARTDATA-IRAF package are consistent with the input values, their SExtractor estimates are ≈15% higher. This drawback can be dealt with following the Foucaud et al. (2007) prescriptions of generating mock images with correspondingly diminished input values of the FWHM. The results of these simulations are presented in Sec. 4.1, where we discuss the completeness of the catalogs. The PSF spatial variation exemplified in Fig.4 is by far the most important effect which might complicate the correct choice of CLASS STAR. In fact, while the resulting CLASS STAR is highly sensitive to the SEEING FWHM keyword, up to the last release of SExtractor has this keyword fixed for the whole frame. This means that, in case of strong space variations of the FWHM, SExtractor will tend to overestimate the number of stars (galaxies) in the mosaic regions where the local FWHM is greater (lower) than the median value used for source extraction. In Fig.5 we present, for all our clusters in both bands, the distribution of the quantity: τ = FWHMMIN · (FWHMMAX − FWHMMIN )

(1)

where FWHM max/min are the maximum and the minimum median FWHM of the single stacked detectors, respectively, as determined from grids like those in Fig.3. The value of τ is a quality and stability indicator of the PSF for complete mosaics and all the observations collected during a given night. Fig.5 shows that the majority of our clusters have relatively low values (high quality) of this quantity (τ < 0.25; marked by the vertical dotted line). In clusters with τ > 0.25 the choice of the SEEING FWHM keyword and other parameters is very difficult, due to many misclassifications being found, and the use of the interactive cleaning procedure becomes fundamental to generate reliable stars and galaxies catalogs (see Fig.6).

Fig. 6. Plot of the difference between two aperture magnitudes (1 arcsec - 3 arcsec diameter) vs. total magnitude for galaxies (blue bold dots) and stars (red light dots) as classified by SExtractor before any interactive cleaning. In this diagram the star and galaxy loci turn out to be well apart down to J≈19.0. In the good cases (top panel: A1069, with quite stable seeing during observations, τ = 0.21), only a few galaxies are misclassified, and even at bright magnitudes. Alternatively, in cases of strong seeing variations (bottom panel: A1795, τ = 0.32), many more misclassifications of galaxies may be found (bold dots in the locus of stars).

3.2. Interactive Cleaning

Spurious detections are most frequently found along the overlapping regions of the detectors, along the mosaic edges (which are excluded by the initial polygon mask) and in (usually limited) regions of high background fluctuations. Cross-talk due to saturated stars is another cause of spurious objects. These false detections can be found at fixed distances (in symmetric positions) from the saturated objects along the read-out direction of the detector, and have a characteristic “doughnut” shape (see, i.e., Dye et al. 2006). It was not possible to safely detect and delete them automatically, as their number and occurrence is not simply correlated with the peak intensity and the area of the saturated object, and in the final mosaic they can be found either in the X or in the Y direction. On the other hand, by exploiting the SExtractor classification capabilities, it was found that only the brightest ones are misclassified as galaxies, and most of them are easily deleted by the interactive cleaning procedure described below. Since the automated pipeline produces a separate catalog of the saturated stars, during the final visual check, we highlight them, allowing easy identification and deletion from the final catalogs of their associated cross-talk false detections. A negligible fraction of these objects, depending on the number of saturated stars in the mosaic, may remain after this last check, but are mainly classified as “unknown” sources. Since the number of objects is too large for any individual analysis, we purged spurious detections and star/galaxy mis-


classifications with our interactive tool, which generates several plots of different combinations of parameters, like: – µmax vs. J,K , where µmax is the surface brightness of the brightest pixel of an object and J,K is the total apparent magnitude MAG AUTO. – J,K vs. log10 (I sophotal Area), where I sophotal Area is the area in pixels of each object at the threshold level. – log(FWHM) vs. log10 (I sophotal Area). – J,K( d ≤ 1′′ )-J,K( d ≤ 3′′ ) vs. J,K, where d is the aperture diameter. In all these diagrams, the stars populate a rather narrow and well defined region, while galaxies are more spread throughout the plane (see also, Paper-II). As an example, in Fig.6 a plot of the difference between two aperture magnitudes (1 arcsec 3 arcsec diameter) vs. total J magnitude for galaxies (blue bold dots) and stars (red light dots) before any interactive cleaning, is presented. It is apparent that for the cluster A1795, due to seeing variations and background conditions, a considerable amount of objects with CLASS STAR typical of galaxies actually populate the region of stars. In the case of A1069, the more stable PSF causes a better classification. Still, at faint magnitudes, a nonnegligible amount of misclassifications is left. Thus, a further step turns out to be necessary in any case to clean the catalogs. To this aim, the outliers and/or misclassifications highlighted by these diagrams are interactively selected, and, if wanted, the cleaning pipeline shows a tile-mosaic of these objects for visual inspection to easily select spurious detections. Then, simple commands allow us to look at their location in the original mosaics, list their parameters from the catalog, delete them or change their classification. At the end of this process the degree of misclassification of relatively bright sources (J < 18.5 and K < 18.0) is less than 1%, that is practically negligible (see Sec. 4.1) . Of course, going to fainter and smaller objects the regions occupied by stars and galaxies start to mix up, making the classification more and more unreliable. The published catalogs will be regularly updated to correct for any newly found spurious objects and/or misclassifications, therefore users are encouraged to rely upon the latest available version of the catalogs. 3.3. Catalogs Description

In Tab.2 we present an example of the entries in the nearinfrared photometric catalogs. The parameters stored for each object are the following (in parenthesis we give the name of the SExtractor’s output parameter): – ID: objects internal identification, it is unique for all catalogs of the WINGS survey. – (αBary ,δBary ): equatorial coordinates (J2000.0) of the barycenter. – (αPeak ,δPeak ): equatorial coordinates (J2000.0) of the brightest pixel. – Area: area at the detection threshold. – rKron : Kron radius used to compute the MAG AUTO magnitude. – FWHM: full width at half maximum assuming a gaussian core as calculated by SExtractor. – b/a : axis ratio of the source. – PA : position angle of the major axis (North=0o, measured counter-clockwise). – µmax : Surface brightness of the brightest pixel. – MAG BEST : SExtractor’s best total magnitude estimate.

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– MAG AUTO : SExtractor’s Kron (total) aperture magnitude. – MAG(4.3kpc), MAG(10.8kpc), MAG(21.5kpc): magnitudes within apertures of diameter 4.3kpc, 10.8kpc and 21.5kpc, respectively, measured at the clusters’ redshift. Since we will adopt in future papers of the survey H0 = 70 (at variance with Paper-II, where H0 = 75 has been used), the apertures in kpc are slightly different from those given in Paper-II (4/10/20 kpc): however, the value in arcseconds remains the same. – MAG(0.′′7), MAG(1.′′ 0), MAG(1.′′ 6)...: magnitudes measured within 8 different fixed apertures8 in arcsec. – FLAG SEX : SExtractor’s FLAG keyword. – CLASS STAR : SExtractor’s stellarity index. – WINGS FLAG: summarizing flag column reporting pipeline classification and photometric quality of the objects, using the following prescription: WINGS FLAG = a1 + 2a2 + 4a3 + 8a4 + 16a5 + 32a6 a1 = 1 if classified as star a2 = 1 if classified as galaxy a3 = 1 if classified as unknown a4 = 1 if weakly affected by neighbouring halo a5 = 1 if strongly affected by neighbouring halo a6 = 1 if in an area of confidence 18.0 the frequency of such misclassification becomes relevant (see also Fig.6) and rapidly increases. However, we have to consider that, for faint magnitudes, the galaxy population becomes gradually dominant (field galaxies) and the contribution to the galaxy counts coming from the (usually small) fraction of misclassified stars should be in any case negligible (see, for e.g., Berta et al. 2006). An empirical (a posteriori) check of completeness can be performed, for each cluster in each band, comparing the number counts of classified objects in our catalogs, with the expected number counts of field galaxies and stars. In Fig.8 we show representative cases for J- (left panels) and K-band (right panels). The red dashed lines are the expected number counts of stars in the area of the cluster, calculated with the TRILEGAL code (Girardi et al. 2005) in the photometric system of WFCAM. The black solid lines are the number counts of field galaxies taken from the UKIDSS Ultra Deep Survey (see, Hartley et al. 2008; Lawrence et al. 2007). The open red and the full blue dots are the WINGS number counts for stars and galaxies, respectively, as calculated from our source lists, with their Poissonian error bars. In general, the agreement between the expected star counts and our catalogs is outstanding, confirming the excellent performance of the TRILEGAL code as a model of the Galaxy. However, for 6 clusters out of 28, we noticed an excess in the real star counts at magnitudes brighter than 18 (see for instance A1644 in Fig.8). Since all these clusters are found to lay approximately in the direction of the center of the Galaxy (albeit with |b| ≥ 20o ), where a higher fraction of halo stars is encountered, we assume that the model might slightly break down in this direction. Our galaxy counts agree with the field number counts only at faint magnitudes, as expected, showing the presence of cluster galaxies at brighter magnitudes. The excess of number counts shown in Fig.8, revealing the cluster members, can also be seen in Fig.9, where the number counts of all the detected sources (full line), is compared with the cumulative (stars+field galaxies) counts expected for the area of the clusters (dashed line). The approximate magnitude where the


Fig. 8. Number counts in J- (left panels) and K-band (right panels) compared with TRILEGAL models of the Galactic stellar distribution from Girardi et al. (2005) (red dashed lines) and field galaxy counts from UKIDSS-UDS Hartley et al. (2008) (solid black lines). The blue full and the red open dots are the galaxy and stars number counts of the corresponding WINGS catalogs. Poissonian error bars are mostly of the dimension of the symbols. turnover in the counts occur is a good estimate of the observed completeness limit, and is reported in Tab.3, together with the theoretical magnitude limit (dotted-dashed vertical lines) calculated with the following formula: mlim = ZPT − 2.5 log10 [ν (σBG + 1) Amin ]

Fig. 9. Magnitude counts of all detections in J- (left panels) and K-band (right panels), for the deepest (upper panels) and the shallowest (bottom panels) cluster mosaics. The dash-dotted vertical lines correspond to the estimated theoretical magnitude limits from Eq.(2); the turnovers in the counts given in this figure indicate the detection completeness for these cluster images. Both values are reported in Tab.4 for all clusters. The dashed line is the cumulative stars+field galaxies distribution expected to be found in the area of the mosaic.

ideal combination is achieved, objects are detected down to the completeness limit without populating the source lists with unwanted spurious detections.

(2)

where ν is the relative threshold cut in units of background rms (σBG ), Amin is the minimum number of contiguous pixels required for detection and ZPT is the mosaic zero point magnitude, normalized to one second exposure time and airmass corrected: ZPT = MAGZPT + 2.5 log10 (5) − (χ − 1) · k

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(3)

where MAGZPT is the zero-point magnitude keyword found in the FITS header of the mosaic, resulting from the calibration with 2MASS performed at CASU. The second refers to the WFCAM 5s exposure time, the term χ and k variables are the average airmass and extinction (keywords AIRMASS and EXTINCT in FITS file), respectively. This theoretical limit corresponds to the magnitude of an object consisting of Amin contiguous pixels with ADU counts of ν(σBG + 1), and gives an idea of the overall depth of the mosaic imaging, since it links photometric and detection properties together. The dash-dotted vertical lines in Fig.9 show that these limiting magnitude values are quite consistent with the faintest detected objects. At the final step, the consistency among the previous three diagrams (Figs. 9 7 and 8) is used as an a posteriori quality check, as well as as a good empirical tool for possible refinement of SExtractor’s input parameters. Usually, when this

4.2. Astrometry

Most of the non-linear distortion of WFCAM over the entire field is accounted for, by a cubic radial term in the astrometric solution. The CASU pipeline processes the raw images considering the differential field distortion too, giving at the end an astrometric error usually below 50mas9 (for further details refer to CASU website, and Dye et al. 2006). We are able to reach that precision only in relative astrometry, i.e. difference of coordinates in two bands of the same field. After the coadding of all the MEFs into a single mosaic, an additional astrometric and photometric re-calibration check with point-like sources from the 2MASS catalogue is performed. Fig.10 is a visualization of the astrometric precision and accuracy for our entire cluster collection: upper panels show the overall spread of the right ascension and declination differences between the positions of common point-like sources in our catalogs and in 2MASS (left panel) or UCAC2 (right panel). Only sources with a photometric error lower than 0.1 mag were considered. The overall zero point shift is negligible for all our applications, and the rms is consistent with UKIRT-WFCAM standard requirements, being of the order of 100mas (RMS=112mas). 9

milliarcseconds.

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Fig. 10. Astrometry of the WINGS-NIR survey, compared with 2MASS point-source and UCAC2 catalogs (upper panels) and histograms of the distances (bottom panels). The overall precision is of the order of 100mas and nearly 70% of the offsets calculated are below one pixel distance. The high precision astrometry of our survey allows safe cross-matching of catalogues extracted from other surveys. While the original stacks of each tile use a Zenithal Polynomial projection (ZPN) astrometric solution, the final coadded mosaics are expressed in the standard gnomonic tangential projection (TAN). Thanks to the accurate and precise astrometric solutions applied to our mosaics, cross-matched source lists in different bands (optical and U bands included) are very easily obtained, and the fraction of mismatches is negligible. 4.3. Photometry

In Tab.4 a summary of the properties of the catalogs is given, and the surface brightness limits, calculated in the following way, are reported: µlim = ZPT − 2.5 log10 [ν σBG ] + 2.5 log10 [A′′ ]

(4)

where A′′ is the pixel area in arcseconds. This relation gives the minimum surface brightness a pixel can have due to the sigma clipping chosen for the specific mosaic. The output of Eq.(4) is obviously changing from cluster to cluster, but it generally settles at µJlim ≈ 22 and µKlim ≈ 21. Photometry at CASU is currently based on 2MASS, via colour equations converting 2MASS magnitudes to the WFCAM photometric system. The most recently released photometric calibrations are given by (see, Hodgkin et al. 2009). Neglecting the interstellar extinction, they are as follows: JWFCAM = J2MASS − 0.065(J2MASS − H2MASS ) KWFCAM = K2MASS + 0.010(J2MASS − K2MASS )

(5) (6)

Due to the improved precision in the fitting algorithm, these equations are different from those given in the early-data-release

Fig. 11. The 2MASS photometry is converted to the WINGS/WFCAM photometric system with Eqs.(5,6) and then compared with WINGS magnitudes, for the whole collection of our NIR mosaics, separately in J- (upper panel) and K-band (lower panel). For a correct comparison with 2MASS we use aperture (3.′′ 0 diameter) corrected magnitudes in the Y axis. The absence of a significant zero point shift and the relative tightness of the magnitude sequence demonstrates the quality of the photometry in the WINGS survey. by Dye et al. (2006). Different tests carried out at CASU suggest that the 2MASS calibration is delivering photometric zero-points at the ±2% level, confirming the excellent results achieved by the 2MASS survey team in ensuring a reliable all-sky accurate calibration. While coadding the single MEFs to obtain the final mosaic, a photometric re-calibration check based on 2MASS catalogs is performed again, to assure a spatially homogeneous zero point throughout the mosaic. Fig.11 shows the difference between 2MASS and WINGS aperture corrected magnitudes of point-like sources (after applying Eqs.5 and 6), vs. WINGS total magnitudes in the J (upper panel) and K (lower panel) bands, for the complete survey. No significant zero point shift is recovered, confirming the accuracy of the photometric calibration of our WINGS-NIR survey. Photometric errors assigned by SExtractor are unrealistically small, because they are based only on the photon-noise statistics. The use of the so called confidence-maps (weightmaps for SExtractor users) helps in computing more realistic values of photometric uncertainties. However, we have verified that this latter approach still leads to underestimates of the photometric errors. Therefore, we preferred to use the simulations to recover the effective precision of our measurements. Nevertheless, SExtractor errors will be available in the WINGS website queries forms. In Fig.12 we present the global results (J and K band together) of photometry checks from the simulations. In the top panel the differences between input and output SExtractor magnitudes as measured from the mock images for stars (red starred dots), exponential disks (open magenta circles) and


Fig. 12. Global photometric precision and accuracy as obtained from simulations for stars (red starred dots), disks (open magenta circles) and spheroids (full blue circles). (Top panel) average differences between the input and SExtractor magnitudes for bins of 0.5mag of input total magnitude. (Bottom panel) rms of the previous differences. While the magnitudes of stars and disks are perfectly recovered, there is a systematic shift of 0.2mag in the spheroidal r1/4 magnitudes, due to the loss of light in the wings of simulated galaxies. spheroidal r1/4 (blue filled circles) are plotted for 0.5 mag bins of total magnitude. The agreement is quite good, apart from the constant shift of 0.2mag for spheroidal galaxies even at bright magnitudes. This effect is well known from the literature (Franceschini et al. 1998) and is also recalled in Paper-II. It is caused by the loss of light from the wings of profiles of spheroidal r1/4 galaxies in the background noise. The bottom panel of the same figure presents the overall precision of our photometry (the RMS of measurements is plotted for 0.5mag bins, the symbols are as above), with the corresponding polynomial fit for spheroids (blue full line) and exponential disks (magenta dashed line) to be used as the RMS value for all magnitudes of the catalogs: RMSearly = −3.675908 + 0.778114mearly + −0.054269m2early

+

Fig. 13. Color magnitude diagrams of classified galaxies for a selection of WINGS-NIR clusters. The relatively thin red cluster sequences confirm the accuracy of the photometry provided by SExtractor. The large number of detections obtained at fainter magnitudes are the field background galaxies and demonstrate the deepness of our survey. There are also indications of red sequences for the field galaxies at bright magnitudes, indicating the presence of background clusters at higher redshift. The big red full circles are the spectroscopically selected members of the clusters (Cava et al. 2009). pending on overall quality of the image. Given the overall rates of successful classification deduced from Fig.7 and Tab.3, it is more than conservative to assign those levels of photometric errors beyond 18.0 mag and 17.5 mag for the J- and K-band, respectively. As an internal check of photometric accuracy, we show in Fig.13 two representative examples of total MAG AUTO colormagnitude diagrams of galaxies from our survey. The tightness of the red sequences (the RMS of the spectroscopically confirmed members of the clusters is 0.03mag and 0.05mag) demonstrates the internal consistency of the WINGS-NIR photometry (these RMS values are found also by Eisenhardt et al. 2007).

(7)

5. Summary

0.001263m3early

RMSlate = −3.765820 + 0.806960mlate + −0.057414m2late + 0.001360m3late

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(8)

where mearly/late can be either the total or the aperture magnitude of the corresponding object. Due to the way these errors have been computed, the statistical Poissonian error can be ignored. Moreover Eq.7 is adequate for the errors associated with the “unknown” objects category, while Eq.8 can safely be used for point like sources. Fig.12 shows that the global rms is below 0.1 mag down to 18.3 mag for stars and disks, and almost 18.0 mag for spheroids. It is clear that for J-band mosaics this limit can sometimes reach 18.8 mag, while for the K-band it is found at ≈17.5 mag, de-

In this paper we have presented the first data release of the WINGS-NIR survey, comprised of J- and K-band photometric catalogs for a sub-sample of 28 nearby galaxy clusters belonging to the WINGS optical survey. The detected sources have been classified as stars, galaxies and unknown objects, and for each of them we give positions, geometrical parameters and different kinds of total and aperture magnitudes in both bands (when available). Due to the variations of the seeing FWHM over the large areas of our mosaics and the poor reliability of SExtractor’s CLASS STAR parameter, we used a specifically designed interactive pipeline to improve star/galaxy classification. The nearinfrared WINGS data consists of nearly one million detected

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sources, with 150,000 and 500,000 reliably classified stars and galaxies, respectively. This unique collection of data turns out to be 90% complete in detection limits at J=20.5 and K=19.4, and in classification limits at J=19.0 and K=18.5 (limits vary from cluster to cluster depending on seeing conditions and total integration time), values to be compared with 2MASS, J=15.8 and K=14.3 for point-like sources, UKIDSS Large Area Survey J=20.0 and K=18.4, and UKIDSS Ultra Deep Survey J=25.0 and K=23.0 (for further details, see Skrutskie et al. 2006; Dye et al. 2006; Lawrence et al. 2007). Using this new, extensive and comprehensive catalog, we will study several different properties and characteristics of lowredshift cluster galaxies. The near-infrared data presented in this paper will be combined with our optical photometry, morphology and spectroscopy catalogs to systematically study the dependence of these cluster galaxy properties on their stellar mass (since this is traced by the J and K bandpasses). Furthermore, the study of the J,K luminosity functions (known to be a good tracers of the stellar mass function), will allow us to estimate the distribution of the stellar mass-to-light ratio as a function of the cluster-centric distance, for different morphological types. For the galaxies in our sample for which we can derive reliable nearinfrared surface photometry and structural parameters (e.g., Re , < µe>), we will investigate the behaviour of our sample with respect to the various scaling relations (i.e., Fundamental Plane, Kormendy relation). Broad-band spectral energy distributions will be generated for the galaxies in our sample by combining the optical and near-infrared photometry, giving us further information on stellar content and cluster membership. Also, using our cluster sample, we will be looking at what effect the use of a near-infrared total cluster luminosity has on the Fundamental Plane of Galaxy Clusters. The final version of the complete WINGS survey catalogs will be a comprehensive cross-matched source-list, allowing multiple criteria queries with Web based tools and EuroVO facilities. Through the website the identification of objects which have measurements and calculated quantities from different branches of the survey (U,B,V,J,K photometry, surface photometry, morphology and spectroscopy) will be made easier by means of specific web applets. Acknowledgements. T. Valentinuzzi acknowledges a post-doc fellowship from the Ministero dell’Istruzione, dell’Università e della Ricerca (Italy). He also thanks for help and useful discussions: Omar Almaini, Sebastien Foucaud, Roberto Caimmi, Stefano Berta, Luca Rizzi, Alessia Moretti, and Stefano Rubele. D. Woods acknowledges the OPTICON travel funding scheme and Australian Gemini Office research funds both which made it possible to obtain these observations at UKIRT. He also thanks his Padova collaborators for their hospitality and generosity during his visit for the team meeting.Marco Riello and Tiziano Valentinuzzi would like to thank Mike Irwin for making the stand-alone versions of the CASU pipeline software available and for helpful discussions and suggestions on the data processing issues related to this work. We also thank the unknown referee for useful suggestions and comments which stimulated discussion and resulted in an improved paper. These observations have been funded by the Optical Infrared Coordination network (OPTICON), a major international collaboration supported by the Research Infrastructures Programme of the European Commission?s Sixth Framework Programme. We want to acknowledge the Terapix software group for the immense help bestowed by their software utilities, and CASU for the great job done with prereduction of images and in keeping databases. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. IRAF (Image Reduction and Analysis Facility) is written and supported by the IRAF programming group at the National Optical Astronomy Observatories (NOAO) in Tucson, Arizona. NOAO is operated by the Association of

Universities for Research in Astronomy (AURA), Inc. under cooperative agreement with the National Science Foundation.


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Table 3. WINGS-NIR clusters sample with relevant useful quantities.

0.0442 0.0484

Lx/1044 erg s−1 1.65 0.71

Pixel kpc 0.174 0.190

-22:06:39.0

0.0670

0.72

0.256

07 53 26.6

+29 21 34.5

0.0619

0.57

0.238

A957x

10:13:38.3

-00:55:31.3

0.0436

0.40

0.172

A970

10:17:25.7

-10:41:20.2

0.0587

0.77

0.228

A1069

10:39:43.4

-08:41:12.4

0.0650

0.48

0.250

A1291

11:32:23.2

+55:58:03.0

0.0527

0.22

0.206

A1631a

12:52:52.6

-15:24:47.8

0.0462

0.37

0.182

A1644

12:52:52.6

-15:24:47.8

0.0473

1.80

0.186

A1795

13:48:52.5

+26:35:34.6

0.0625

5.67

0.240

A1831

13:59:15.1

+27:58:34.5

0.0615

0.97

0.238

A1983

14:52:55.3

+16:42:10.6

0.0436

0.24

0.172

A1991

14:52:55.3

+16:42:10.6

0.0587

0.69

0.228

A2107

15:39:39.0

+21:46:58.0

0.0412

0.56

0.162

A2124

15:44:59.0

+36:06:33.9

0.0656

0.69

0.252

A2149

16:01.28.1

+53:56:50.4

0.0679

0.42

0.260

A2169

16:13:58.1

+49:11:22.4

0.0586

0.23

0.226

A2382 A2399 A2457 A2572a A2589

21:51:55.6 21:57:01.7 22:35:40.8 23:17:12.0 23:23:57.5

-15:42:21.3 -07:50:22.0 +01:29:05.9 +18:42:04.7 +16:46:38.3

0.0618 0.0579 0.0594 0.0403 0.0414

0.46 0.51 0.73 0.52 0.95

0.238 0.224 0.230 0.160 0.164

IIZW108

21:13:55.9

+02:33:55.4

0.0493

1.12

0.192

MKW3s

15:21:51.9

+07:42:32.1

0.0450

1.37

0.176

RX1022

10:22:10.0

+38:31:23.9

0.0491

0.18

0.192

RX1740

17:40:32.1

+35:38:46.1

0.0430

0.26

0.170

Z8338

18:11:05.2

+49:54:33.7

0.0473

0.40

0.186

Cluster

DEC dd:mm:ss -08:41:12.4 +36:54:19.2

Redshift

A119 A376

RA hh:mm:ss 10:39:43.4 02:46:03.9

A500

04:38:52.5

A602

RA, DEC: coordinates of the Brightest Cluster Galaxy Redshift: from NED. Lx: X-ray luminosity Pixel: pixel size in kpc at the given redshift. RUN: semester and passband of observation. FWHM: minimum and maximum estimation as shown in Fig.4. Stacks: Interleave Stacks per mosaic used. Total Area: the effective area of the mosaic.

RUN 05B-K 05B-J 05B-J 05B-K 06B-K 05A-J 06B-K 06B-J 05A-K 05A-J 05A-K 06B-J 05A-K 05A-J 05A-K 06B-J 05A-K 05A-J 05A-K 06B-J 05A-K 06B-J 05A-K 05A-J 06A-J 05A-K 05A-J 06A-K 06A-K 06A-J 05A-K 05A-K 05B-K 05B-K 06A-K 06A-K 05B-J 05A-K 06B-J 06A-K 05B-J 06B-K 05A-K 06A-J 05A-K

FWHM” min max 0.79 0.88 1.05 1.17 1.28 1.44 1.21 1.42 0.88 0.94 1.02 1.47 0.75 0.92 0.91 1.11 1.06 1.26 0.98 1.19 0.88 1.12 0.80 0.93 0.99 1.15 0.89 1.15 0.82 1.10 0.88 0.98 0.89 1.15 1.42 1.65 0.95 1.57 0.72 0.91 0.93 1.07 0.90 1.03 0.79 0.98 0.88 1.00 0.95 1.19 0.85 1.03 1.38 1.18 0.80 1.03 0.79 0.98 0.89 1.03 0.83 0.96 0.89 1.25 0.91 1.14 0.84 1.00 0.79 0.94 0.87 1.07 0.98 1.66 0.87 1.03 0.92 1.03 0.88 1.06 0.91 1.22 0.80 0.95 0.91 1.23 0.80 0.95 0.79 0.92

Stacks 16 8 8 16 12 8 8 8 16 8 16 8 16 8 16 8 16 8 8 12 16 8 16 8 8 16 8 16 20 8 16 16 16 16 16 16 8 16 8 16 8 8 12 8 16

Total Area deg2 Mpc2 0.781 7.66 0.777 9.09 0.776

16.48

0.778 0.779 0.782 0.777 0.775 0.783 0.782

14.29 7.47 7.50 13.09 13.05

0.778

10.70

0.781 0.780 0.778 0.772 0.780 0.387 0.778 0.779 0.779 0.772 0.779 0.778 0.748 0.780 0.782 0.781 0.780 0.771 0.770 0.781 0.780 0.781 0.781 0.777 0.780 0.780 0.781 0.779 0.782 0.778 0.778

15.86

8.38 8.72 8.65 14.56 7.22 14.23 7.47 7.40 13.12 6.62 6.36 16.05 16.09 17.11 12.91 12.76 14.13 12.70 13.37 6.48 6.81 9.28 9.32 7.83 9.30 9.34 7.28 8.72

14


Table 4. WINGS-NIR catalogs useful parameters. Cluster

DM

A119 A376

36.46 36.66

A500

37.40

A602

37.22

A957x

36.43

A970

37.10

A1069

37.33

A1291

36.85

A1631a

36.56

A1644

36.61

A1795

37.24

A1831

37.20

A1983

36.43

A1991

37.10

A2107

36.30

A2124

37.35

A2149

37.43

A2169

37.09

A2382 A2399 A2457 A2572a A2589

37.21 37.07 37.12 36.25 36.31

IIZW108

36.70

MKW3s

36.50

RX1022

36.69

RX1740

36.40

Z8338

36.61

Band mag K J J K K J K J K J K J K J K J K J K J K J K J J K J K K J K K K K K K J K J K J K K J K

mlim mag 21.2 21.5 21.6 20.8 21.2 21.5 21.1 21.8 20.9 21.9 20.6 21.8 20.6 21.2 20.7 21.8 20.6 22.0 20.8 21.7 20.5 21.9 20.2 21.7 21.7 20.8 21.7 21.1 20.9 21.7 20.6 20.3 20.8 20.9 21.0 21.1 21.5 21.03 21.9 21.0 21.5 21.2 20.6 21.7 20.7

µthresh mag/′′2 21.02 22.11 21.93 21.02 21.68 22.11 21.49 22.31 21.13 21.95 21.32 22.37 21.29 22.09 21.02 21.98 20.90 22.27 21.07 22.34 21.19 22.63 20.74 22.10 22.22 21.08 22.29 21.36 21.11 22.14 20.99 20.57 21.04 21.14 21.25 21.36 21.83 21.32 22.56 21.35 22.10 21.53 20.86 22.16 21.06

Cum Det 20.0 20.2 21.0 19.7 20.0 20.3 20.0 21.0 19.7 21.2 19.2 21.0 19.5 20.0 19.1 20.2 19.0 21.5 19.0 21.1 19.3 20.6 18.9 21.1 21.2 19.2 21.2 20.0 20.0 21.0 19.7 19.5 19.6 19.8 19.9 20.0 20.4 19.8 20.5 20.0 20.2 20.1 19.1 20.9 19.5

Stars Det Clas 19.8 18.2 20.4 19.0 20.0 18.0 19.2 17.7 20.3 18.7 20.3 18.5 20.0 18.7 20.6 18.6 19.5 17.7 20.2 18.6 20.3 18.7 21.1 19.2 19.5 18.2 20.1 18.5 19.7 18.4 20.7 19.2 19.5 18.1 20.1 18.0 19.1 18.0 21.0 19.5 19.3 17.5 21.0 19.5 19.0 18.0 20.8 19.0 20.7 19.1 19.2 18.0 20.2 18.7 19.8 18.3 19.7 18.3 20.8 19.1 19.5 18.2 19.2 17.7 19.5 18.2 19.7 18.0 19.7 18.4 19.8 18.0 20.5 19.0 19.8 18.3 21.0 19.6 19.7 18.3 20.5 18.9 19.8 18.2 19.1 17.8 20.7 19.2 19.6 18.6

Gals Det Clas 19.3 19.0 20.0 19.6 19.6 19.2 18.7 18.3 19.3 19.2 19.8 19.4 19.7 19.4 20.0 19.5 18.9 18.5 19.8 19.5 19.7 19.4 20.3 20.2 19.0 18.8 19.5 19.1 19.3 18.7 20.0 19.6 18.9 18.7 19.8 18.0 18.7 18.5 20.5 20.1 19.0 18.6 20.3 20.0 18.5 18.5 20.0 19.5 20.0 19.6 19.3 19.1 20.0 19.7 19.2 19.1 19.3 19.0 20.1 19.8 19.3 18.9 18.8 18.3 19.2 18.6 19.3 18.7 19.3 19.2 19.5 18.8 19.8 19.0 19.5 19.0 20.3 20.0 19.3 18.7 19.9 19.6 19.3 19.2 18.7 18.2 20.0 19.9 19.3 18.9

Notes

1 1,2 2,4 1,3 2,4 2 1,4 5,6

1,2,5 1,3 1,4 1,4,6 2,5 2 6 1,6 1,4 4 2 1,4

6

2 1,4 4 2,5 2,5 1,2,4 1,4,6 1 2,4 2 2 1,2,4 2

The Cluster column identifies the cluster, the DM column is the cluster distance modulus in magnitudes, the mlim column is the theoretical magnitude detection limit calculated with Eq.(2), µthresh column is the minimum surface brightness at the detection threshold cut calculate with Eq.(4), the cumulative detection column (“Cum Det”) lists the turnover magnitude of the number counts diagram for all the detected sources (see Fig.9), “Stars” and “Gals” columns represent the average 90% completeness detection and classification limits for stars and galaxies as deduced from simulations. NOTES LEGEND: 1:= patchy and/or noisy background, 2:= strongly variable PSF, 3:= many spurious detections 4:= excess of classified stars, 5:= deficient in classified stars, 6:= exceptionally bright star(s)


15

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